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When Mice Consume Like Elephants: Instant Messaging Applications, 2014, E-Energy '14: Proceedings of the 5Th International Conference on Future Energy Systems, 97-107

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When Mice Consume Like Elephants: Instant Messaging Applications, 2014, E-Energy '14: Proceedings of the 5Th International Conference on Future Energy Systems, 97-107 When Mice Consume Like Elephants: Instant Messaging Applications Ekhiotz Jon Vergara Alonso, Simon Andersson and Simin Nadjm-Tehrani Linköping University Post Print N.B.: When citing this work, cite the original article. "© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published: Ekhiotz Jon Vergara Alonso, Simon Andersson and Simin Nadjm-Tehrani, When Mice Consume Like Elephants: Instant Messaging Applications, 2014, e-Energy '14: Proceedings of the 5th international conference on Future energy systems, 97-107. http://dx.doi.org/10.1145/2602044.2602054 Postprint available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108223 When Mice Consume Like Elephants: Instant Messaging Applications Ekhiotz Jon Vergara, Simon Andersson, Simin Nadjm-Tehrani Department of Computer and Information Science Linköping University, Sweden [email protected], [email protected], [email protected] ABSTRACT 1. INTRODUCTION A recent surge in the usage of instant messaging (IM) appli- Instant messaging (IM) applications have emerged as the cations on mobile devices has brought the energy efficiency substitute for Short Message Service (SMS) and have gained of these applications into focus of attention. Although IM wide popularity. These applications offer the possibility of applications are changing the message communication land- sending text messages (1-to-1 or to a group) as well as other scape, this work illustrates that the current versions of IM multimedia messages (e.g., images, audio or video). Appli- applications differ vastly in energy consumption when using cations such as WhatsApp or QQ have already 400 and 800 the third generation (3G) cellular communication. This pa- million online users respectively [3, 4], and recently, IM has per shows the interdependency between energy consumption overtaken the traditional SMS text messages [1]. Given the and IM data patterns in this context. widespread use of IM, even home appliance manufacturers We analyse the user interaction pattern using a IM dataset, envision its usage for controlling their equipment [2]. consisting of 1043370 messages collected from 51 mobile While this might seem a blessing to the user, IM text mes- users. Based on the usage characteristics, we propose a mes- sages are an example of a type of traffic with low bandwidth sage bundling technique that aggregates consecutive mes- requirement, which leads to high energy consumption. The sages over time, reducing the energy consumption with a exchange of a couple of text messages can consume as much trade-off against latency. The results show that message as sending an image due to the radio resource allocation of bundling can save up to 43% in energy consumption while cellular networks. From the cellular network operator per- still maintaining the conversation function. Finally, the en- spective, the signalling overhead created by IM is very high ergy cost of a common functionality used in IM applications given their intermittent and small data transmissions. that informs that the user is currently typing a response, IM applications provide more functionalities than regu- so called typing notification, is evaluated showing an energy lar SMS, such as online presence awareness, typing notifica- increase ranging from 40-104%. tion or status updates. Application developers may unfortu- nately integrate these features without studying the poten- Categories and Subject Descriptors tial impact on energy consumption. A recent study analysed more than 9 million comments from the Google Play Store C.2.1 [Computer Communication Networks]: Wireless and showed that more than 18% of all commented appli- communication; C.4 [Performance of Systems]: Mea- cations have negative comments regarding energy consump- surement techniques tion [28]. The result is that different applications delivering similar General Terms function consume completely different amounts of transmis- sion energy. We selected 6 of the most popular IM appli- Design, Measurement cations from the Play Store on 15th January 2013 as an illustrative example, and sent the same 2 minutes conversa- Keywords tion between two smartphones connected via 3G using the instant messaging; transmission energy; UMTS; mobile de- different applications. The energy consumption for each ap- vices; typing notification plication was computed using EnergyBox [26], our tool that is described briefly in section 6.2. Fig. 1 (top and bottom-right) shows a great diversity regarding the amount of energy spent and data sent by the different applications when performing the short con- versation. The most consuming application (Messenger) Permission to make digital or hard copies of part or all of this work for personal or consumes 153% more energy than the least consuming one classroom use is granted without fee provided that copies are not made or distributed (GTalk) to transmit the same conversation. Fig. 1 (bottom- for profit or commercial advantage, and that copies bear this notice and the full ci- left) shows a significant diversity in the packet size for the tation on the first page. Copyrights for third-party components of this work must be 3 selected applications, which impacts the transmission pat- honored. For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s). tern, and thus the radio resource allocation and energy con- e-Energy’14, June 11–13, 2014, Cambridge, UK. sumption. For example, WhatsApp employs smaller packets ACM 978-1-4503-2819-7/14/06. than GTalk, but performs transmissions more often leading http://dx.doi.org/10.1145/2602044.2602054. 97 100 2. BACKGROUND AND RELATED WORKS 80 We begin by providing an overview on the communication 60 energy footprint for the third generation Universal Mobile 40 (Joules) Telecommunications System (UMTS) at the user equipment 20 (UE) side. The main related works are presented in section Transmission energy 0 WhatsApp Kik Messenger Viber GTalk Skype 2.2. 1 250 0.8 200 2.1 Energy footprint of 3G 0.6 150 The energy consumption of the UE when connected to 0.4 100 a 3G UMTS network is mostly influenced by the radio re- WhatsApp packet size 0.2 Messenger 50 source management performed at the network operator side Empirical CDF of GTalk Data sent (kilobytes) 0 0 by the Radio Network Controller (RNC). The RNC employs 0 500 1000 1500 Kik Packet size (bytes) Viber GTalk Skype the Radio Resource Control (RRC) and Radio Link Control WhatsApp Messenger (RLC) of the UMTS Wideband Code Division Multiple Ac- cess protocols to perform the radio resource management of the UE [11]. Figure 1: Average transmission energy, amount of According to the RRC, the UE implements a state ma- data sent and empirical CDF of packet size for dif- chine where the different states have different power con- ferent Instant Messaging applications exchanging a sumption and performance in terms of maximum data rate short conversation. and latency. The UE states are CELL DCH or Dedicated Channel (DCH), CELL FACH or Forward Access Channel (FACH), and URA PCH or Paging Channel (PCH), sorted to higher energy consumption. Kik, Messenger and Skype from highest to lowest power drain and performance in terms transmit more data than the others, making the 3G interface of data rate and response time. Since the states URA PCH consume more. Using the least energy-efficient application and CELL PCH result in similar energy consumption, we could substantially shorten the battery lifetime of a device, consider them as PCH for simplicity. by a factor of 2.5, and reduce the quality of experience (QoE) for the user. The transmission pattern of IM is mainly determined by 1.5 T2 restart! T2! T2! T2 restart! DCH! 1 user interactions, where interactive traffic is generated by T1! a sequence of exchanged messages. Thus, the complexity of 0.5 FACH! designing energy-efficient transmissions increases given the a Power (Watts) PCH! 0 priori unpredictability of the users. However, studying cur- 0 5 10 15 20 25 30 35 40 rent usage patterns can reveal inefficient ways of performing Time (seconds) transmissions since neither the users nor the applications are 800 aware of the energy footprint characteristics. 600 400 The contributions of our work, which aims to significantly 200 reduce the energy consumption of IM for mobile devices, are Data (bytes) 0 0 5 10 15 20 25 30 35 40 threefold: Time (seconds) • We collect, analyse and provide an IM text message dataset1 of 1043370 messages from 51 mobile users Figure 2: Example power profile for 3G using a mo- that describes users' diverse usage patterns. bile broadband module (Ericsson F3307) and Skype as instant messaging application. • We demonstrate the high energy cost of a network- ing functionality, typing notification, that most IM ap- Fig. 2 shows the observed power consumption levels of plications implement amounting to additional energy the different states at one location for the state machine consumption between 40-104%. implemented by the operator TeliaSonera in Sweden. The bottom graph shows the packets when they were captured • Informed by the usage patterns found in our dataset, a at the network interface of the UE. In PCH, the UE can- message bundling algorithm is proposed showing that not transmit any data, but it can be paged with the lowest aggregating consecutive messages from the same user energy drain. When the UE starts generating or receiving saves up to 43% energy. traffic, some signalling is required to establish the connec- tion and move the UE from PCH to DCH before sending The paper is organised as follows: section 2 explains the any data. background and describes the related works. Section 3 anal- The RNC employs the RLC protocol to evaluate the al- yses the IM dataset of real user messages.
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